Spatial Structure Variability Research Articles

Correlates of spatial structure variability in Bushehr port-city: A comprehensive analysis using fuzzy cognitive mapping methodology

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

Effects of space-confined ratio on the MILD combustion of coke oven gas

The objective of this study is to investigate the effects of the spatial structure on MILD combustion characteristics of coke oven gas via numerical simulation. The space-confined ratio, defined as the ratio of the furnace height to the longest diagonal of the transverse section, is used to characterize the variation in spatial structure. An experimental study in a long-narrow confined furnace (LNCF) was initially conducted to investigate the conditions for achieving MILD combustion. Subsequently, the effects of spatial structure on MILD combustion characteristics were thoroughly investigated through numerical simulation by altering the space-confined ratio of the LNCF. The experimental results show that the transition from conventional combustion to MILD combustion is achieved at a dilution rate of 50 %, which improves the temperature uniformity and reduces the NO emission by 96.5 % to 3 ppm. The numerical results show that reducing the space-confined ratio enhances the flue gas internal recirculation and the temperature uniformity, especially the maximum temperature is significantly reduced. Moreover, reducing the space-confined ratio achieves nearly zero NO emissions, but an excessively space-confined ratio may result in incomplete combustion, consequently causing a significant rise in CO emissions. To guarantee complete combustion, the optimal furnace structure is achieved at a space-confined ratio of 6.4, ensuring not only temperature uniformity but also ultra-low emissions of NO and CO. Therefore, this study provides a theoretical and practical foundation for the ultra-low emission of pollutants in space-confined combustion systems.

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The fifth-generation ECMWF Reanalysis – Land (ERA5-Land) soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to absorb the spatial-structure information of the observed data well and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between the model surface soil moisture and observation increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining these with the model physical processes, the image assimilation system can also gradually improve the spatial structure of soil moisture content at a depth of 7–28 cm, with the spatial correlation coefficient between the model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 d. The results of this study adequately demonstrate the application potential of image assimilation system in short-term climate prediction.

From Grayscale Photopolymerization 3D Printing to Functionally Graded Materials

AbstractFunctionally graded materials (FGMs) have spatial variations in structure or composition that result in gradients in their mechanical, thermal, swelling, or other properties. FGMs have garnered significant research interest because of their extensive applications in areas such as biomedical implants, sensors, and soft robotics. Recently, grayscale photopolymerization 3D printing has emerged as a promising technology for crafting complex and high‐resolution FGMs. This comprehensive work delves into various grayscale photopolymerization 3D printing techniques, offering insights into their ability to precisely control printing exposure energy and polymerization degree, which in turn influence material properties. Furthermore, this work highlights diverse applications of grayscale vat‐photopolymerization 3D printing. Finally, it offers perspectives on ways to enhance grayscale photopolymerization 3D printing, with a focus on improving printing feedstocks and grayscale strategies.

Temporal changes in spatial scale and autocorrelation structure of forest openings based on taxonomic, phylogenetic, and functional turnover

ContextDuring European settlement, Illinois grasslands were converted for agricultural purposes. Remaining natural areas in southern Illinois include natural xeric forest openings, with communities representative of remnant grasslands and adjacent hardwood forests. Previous research in these openings shows plant communities are driven by edaphic conditions. ObjectivesThe first objective aimed to characterize spatial scale and autocorrelation structure of these openings based on climatic, environmental, and diversity variables. The second objective was to predict taxonomic, phylogenetic, and functional turnover between 1988 and 2019, using climatic and environmental variables. MethodsSurveys were conducted to calculate taxonomic, phylogenetics and functional trait metrics and analyses of these dimensions of diversity. Randomization tests were used to assess phylogenetic and functional clustering and over-dispersion at each site. Spatially-explicit climatic and environmental variables were included from earlier surveys and data repositories. Global Moran's I and spatial autocorrelograms were used to assess spatial structure of climatic, environmental, and diversity variables and generalized dissimilarity modeling was used to characterize taxonomic, phylogenetic, and functional turnover based on environmental variables. ResultsSoil depth was the only environmental variable which exhibited significant global spatial autocorrelation. Overall, sandstone sites were phylogenetically over-dispersed while loess sites were phylogenetically clustered. Climate variables and diversity metrics exhibited significant spatial structure during surveys. Generalized dissimilarity models showed that geographic distance between openings was the most influential driver of turnover across surveys. ConclusionsPrevious glacial events explained the spatial structure of soil depth across sites, due to Quaternary loess deposition in loess sites. High diversity values were clustered in the southeastern portions of the study area. Functional generalized dissimilarity models best predicted turnover in these openings compared to taxonomic and phylogenetic models.

Spatial structure of the temporary variability of the Arctic seas surface temperature

Interannual oscillations in the surface temperature of the Arctic Ocean and the North Atlantic with the southern boundary (instead “border”) at latitude 55° 25′ N between 1949 and 2007 are investigated based on the MPIOM (Max Planck Institute Ocean Model) solution. It is a free surface ocean model based on primitive equations in the Boussinesq and incompressibility approximations. High-resolution spectra were estimated via fast Fourier transform with a maximum resolution (Welch’s method). Factor analysis method, which makes it possible to identify areas with highly correlated oscillations and reduce the study of the characteristics in question to their analysis in local points, is used to minimize the significant amount of the initial information about monthly average sea surface temperature fields. Аnalysis of the main factors made it possible to identify 10 areas with quasi-synchronous variability of temperature anomalies by including the points correlated with relevant factors with correlation exceeding 0.6. Spectral structure compliance classification revealed that the areas of the Chukchi Sea, the Hudson Bay, the Irminger Sea, and the Labrador Sea have oscillation peak similarities for the periods of 5–6 years and 8–9 years. Central and western areas of the Norwegian Sea, the area affected by the North Atlantic Current, the eastern part of the Norwegian Sea, and some areas of the Kara Sea have similar spectral structure defined by the peaks at the 11-year and 6-year periods. The Baffin Bay with two main peaks at the 16-year and 5–6-year periods, and the central and the western parts of the Barents Sea, where oscillations are similar to the ones in the Chukchi Sea at short periods, and to the ones in the south-eastern part of the Barents Sea and in the eastern part of the Norwegian Sea at 7–8-year periods, stand out significantly. In some cases, spectrum peaks in different areas appear shifted and attenuated, so presumably the frequency characteristics of the temperature signal change as it moves across the water area.

Ultrasonic assessment of aging in lithium metal pouch cells

Lithium metal anodes are prone to non-uniform lithium plating that can lead to dendrite formation, internal short circuits, and catastrophic ignition. This type of structural defect is not captured by battery management systems, prompting the need to advance nondestructive evaluation (NDE) techniques that are suitable for periodic monitoring of the internal structure. An ultrasonic inspection technique is evaluated here as a means of identifying flaws and irregular lithium plating that can be a precursor to dendrite formation and, ultimately, battery failure.Lithium metal pouch cell batteries were aged through electrochemical cycling at a range of moderate constant-current charging and discharging rates. They were inspected intermittently using a tailored local ultrasonic resonance spectroscopy (LURS) approach that measures the local through-thickness resonances of the battery as an indicator of the underlying structure. LURS results showed clear structural resonances in cells in the range of 3–7 MHz, which shifted significantly to lower frequencies as batteries aged. In addition to point measurements, scanning LURS measurements were implemented to map resonance frequencies over the electrode surface. These resonance maps revealed spatial variations in structure that developed during aging and showed better contrast to structural variation than achieved using conventional amplitude-based c-scans of the specimens.NDE results from pouch cell measurements were compared to a destructive physical analysis of the electrode structures using scanning electron microscopy (SEM). The SEM results showed differences in the end-of-life lithium anode structure at different charge rates. The results of this study show the capability of LURS as a means for identifying changes in battery structures during aging and establish resonance trends that may be applied in future work for prognostic modeling of batteries in service.

Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation

Abstract. An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Digital soil characteristics mapping for aiding site-specific management practices in the West Nile Delta, Egypt

Soil survey system is the key word for sustainable soil use and management. This study aimed to generate a digital soil mapping (DSM) using various spatial resolutions of freely available digital elevation models (DEMs) i.e., (30 m ALOS “Advanced Land Observing Satellite”, 12.5 m ALOS, 30 m SRTM “Shuttle Radar Topography Mission”, and 90 m SRTM) to assess the spatial variability of soil physio-chemical characteristics in El-Beheira Province, Western Nile Delta. A total of 196 geo-referenced soil profiles were collected from agricultural fields, and the soil's contents of total nitrogen (TN), potassium (K), available phosphorous (P), calcium (Ca), sulfur (S), magnesium (Mg), zinc (Zn), copper (Cu), manganese (Mn), and boron (B) were analyzed. The study revealed that the soil's contents of TN, P, S, B, and Cu were low, and Mg was suspected to be higher than K, leading to an Mg-induced K shortage. The study also found wide-ranging spatial variability structures of inverse distance weight (IDW) interpolated maps for several soil physio-chemical characteristics. The DSM further revealed that poor status was present in 85.4% of TN, 91.7% of P, 76.9% of S, and 87% of both B and Cu. The conclusion is that soil variability, as demonstrated in DSM, outweighs uniform treatment, and this discovery will assist extension workers, scientists, and decision-makers in implementing site-specific nutrient management strategies. Validation of the DSM is recommended for crop and variety-specific nutrient treatment rates. Understanding the geographical variance in the soil is critical for long-term nutrient management and higher output.

Variabilidade espacial de índices multiespectrais biofísicos baseados na heterogeneidade e anisotropia para monitoramento de precisão

ABSTRACT The study aimed to characterize the spatial structure of variability of biophysical indexes of vegetation through images obtained by Unmanned Aerial Vehicles under strong heterogeneity and anisotropy, using geostatistical procedures. Plots with different types and densities of culture were evaluated in a didactic vegetable garden. Five vegetation indexes obtained from aerial multispectral camera images were evaluated parallel with geostatistical analysis and anisotropy investigation for multiscale spatial modeling. For the studied domain, geometric anisotropy was identified for the biometric indexes. The spherical model presented a better fit when anisotropy was not considered, whereas the exponential model had the best performance in the anisotropic analysis. Contrasting targets were better identified in multispectral images and considering anisotropy. The Soil-Adjusted Vegetation Index is recommended for similar applications.

Study on the Spatial Structure of the Complex Network of Population Migration in the Poyang Lake Urban Agglomeration

Population constitutes the foundational element of cities, and population migration drives the transfer of production factors among urban areas. The population migration network serves as an objective representation of intercity interactions, bearing great significance for the analysis of urban network spatial structure. This study focuses on the 10 core cities within the Poyang Lake urban agglomeration. It utilizes population migration data from Tencent’s location-based big data spanning from 2015 to 2018. Employing the point-axis theory from spatial network theory and the directed weighted network theory within the complex network, the study establishes a comprehensive set of network indices and a network model for spatial structure. It investigates the dynamics of population migration networks within the urban agglomeration and considers strategies for enhancing, regulating, or guiding urban agglomeration development to strengthen its overall vitality. The findings indicate that the urban agglomeration displays distinct characteristics of an urban hierarchical sequence and demonstrates gradual improvement in its spatial network development. While network density remains relatively stable across various threshold intervals over an extended period, network connectivity remains weak. Moreover, the urban agglomeration exhibits the lowest degree of centralization, the highest network structure entropy, and limited network connectivity. Migration along the primary power axis within the urban agglomeration remains relatively stable, while the internal network of the urban agglomeration is interconnected through a “core-non-core” network, reflecting near-geographical connection characteristics. Variations in spatial structure are observed, with the spatial network structure following two modes: “weak core city + edge city” and “node city + outer network city”. The trend in network connections diversifies, resulting in both “core-edge” connections and cross-regional connections. In conclusion, the network characteristics of the urban agglomeration surrounding Poyang Lake are consolidated to aid in formulating an optimization plan for the urban agglomeration’s spatial structure. Additionally, these findings serve as a reference for studying the evolution of spatial structures in the other two urban agglomerations within the city agglomeration in the middle reaches of the Yangtze River.

Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes

It is practical that industrial processes are regarded as hybrid systems involving both continuous and discrete variables. Particularly, process discrete variables are usually rather crucial to process normal operations. However, existing data-driven fault diagnosis methods have been rarely applied for industrial processes concerning hybrid system characteristics. In response to this issue, this paper proposes a novel industrial process fault diagnosis approach based on fusing logic rule-based hybrid variable graph neural networks (GNNs) able to learn hybrid variable spatial structures. Therein, continuous variables samples are regarded as learning objects, while logic rule information is utilized to guide the learning process. Specifically, a mixed 0–1 integer linear programming algorithm is used to extract logic rules with fault labels from discrete variable data. Subsequently, a rule classification method is used to classify the logic rules with fault labels into independent and interdependent rule classes which are filed in a rule class library using as the basis for topological connections among data samples. In addition, an edge pruning-based graph optimization is employed to reduce redundant connections of the nodes. It is found that the established hybrid variable GNNs is able to learn the spatial topological structure of hybrid industrial process variables, providing rich heuristic knowledge for process fault classifications so as to effectively improve the fault diagnose performance. Simulation experiments are carried out in an industrial coal gasification process platform and a storage tank systems simulation platform, achieving satisfactory results.

Deformation-Failure Characteristics of Coal with Liquid CO2 Cryogenic-Freezing Process: An Experimental and Digital Study

The aim of this paper is to analyze the deformation-failure degree and microstructure variations in coal under the cryogenic-freezing effect of liquid CO2. In this paper, X-ray CT scanning technology is adopted to measure the microscopic-morphological parameters of coal. Drawing support from the image processing and three-dimensional (3D) visualization functions of Avizo software, 3D spatial structure variation rules, as well as the deformation and permeability parameters, are quantitatively calculated. Under the effect of LCO2 cryogenic freezing, the macroscopic mechanical properties and deformation-failure degree of coal are thoroughly analyzed. The results show that fracture-scale parameters of treated coal are significantly increased, resulting in spatial structure parameters including the coal plug total volume (Vt), fracture network volume (V0), and proportion of fracture network (μ0) to increase by 17.11%, 56.57%, and 55.59%, respectively. A comparison analysis indicates that the coverage area of a single value function from the percolation theoretical model for treated coal plugs becomes larger, and its percolation curves are more intensive; the quantitative coal permeability coefficients are increased to more than 40% on average, which further proves that the permeability of coal by using LCO2 cryogenic freezing is significantly improved. Under the same uniaxial stress loading rate, the peak stress threshold value required by treated coal in the compaction and elastoplastic deformation stage is decreased. The corresponding output acoustic emission energy is apparently increased, owing to the increased brittleness of coal, and deformation failure of coal occurs more easily. Simultaneously, the fracture network and matrix surface of treated coal are more complex, and the corresponding fractal characteristic is obvious. It could be thus concluded that the coal plugs have deformation-failure changes under cryogenic freezing by using LCO2, increasing the proportion of coal microstructure and enhancing coal permeability. Therefore, the capability of gas migration through the coal microstructure becomes easier, which is favorable for coalbed methane recovery.

Difference analysis in terahertz wave propagation in thermochemical nonequilibrium plasma sheath under different hypersonic vehicle shapes

Research on terahertz communication under different vehicles has important guiding significance for the design of future hypersonic vehicle and Radio Frequency (RF) blackout. In this paper, a joint simulation model of plasma flow under thermochemical nonequilibrium state and terahertz transmission is developed to investigate the differences in terahertz wave transmission characteristics under different vehicle shapes and the related mechanisms. By comparing the plasma sheath characteristics and terahertz transmission among HIFIRE-5b (Hypersonic International Flight Research Experimentation-5b), RAM C(Radio Attenuation Measurement C) and ARD (Atmospheric Reentry Demonstrator) vehicles, it is found that the sheath thickness and electron density of ARD vehicles is significantly larger than that of HIFIRE-5b and RAM C vehicles, resulting in greater terahertz wave attenuation. Collision absorption plays a major role in the terahertz attenuation of vehicles, and the contribution of reflection effects is only observed in the ARD vehicle due to its larger plasma sheath thickness and spatial structure variation. Based on the above comparison results, a shape design scheme of reducing the vehicle head and tail for mitigating RF blackout is proposed, and the scheme is proved by further analyzing the effects of different vehicle heads and tails on the terahertz communication. With the decrease of the head radius and tail width of hypersonic vehicle, the wave attenuation at the same terahertz frequency decreases, and the contribution of reflection effect to wave attenuation gradually disappears. Therefore, the shape design scheme of reducing the vehicle head and tail can effectively alleviate the RF blackout problem, which provides an important reference value for future hypersonic vehicle design.

Restoration of woody plant communities in red pine plantations on abandoned agricultural land in northern Minnesota, USA

While tree plantations have increased globally, there is growing interest in restoring plantations to native ecosystems. Restoration efforts may be more successful when tree species native to original ecosystems are planted instead of species that are either introduced or native, but off site.Plantations of red pine (Pinus resinosa Ait.) in the western Great Lakes region of North America were commonly established on red pine sites, often after agricultural clearing and abandonment. Red pine is the only species in the overstory, compared to 10 or more species in red pine woodlands. Plantations differ in structure from woodlands, being single-aged versus multi-cohort and having high and spatially-uniform density versus lower and variable density.Our aim was to contrast changes in structure and woody species composition over a 15-year period in response to two silvicultural approaches in red pine plantations on the Red Lake Wildlife Management Area in northwestern Minnesota, USA. Approaches included: 1) uniform thinning followed by a dispersed retention harvest (dispersed treatment) and 2) variable density thinning followed by an expanding gap irregular shelterwood (aggregated treatment).Basal area of trees ≥10 cm dbh was reduced similarly in both treatments to 9 m2 ha−1, but there was high within-stand variability (coefficient of variation) in the aggregated treatment (>100%) compared to the dispersed treatment (20%). Understory woody species abundance increased with both treatments, including sapling, large regeneration, and small regeneration layers, with >100% increases. In the sapling layer, the increased abundance (to 2.5 m2 ha−1) reflected new recruitment into this layer, as there were few saplings before treatment (<0.5 m2 ha−1). By year 15, the majority of saplings were canopy capable species, while lower regeneration layers contained mixtures of canopy trees, sub-canopy trees, and shrubs.Composition in the understory changed, but was similar between treatments, including increases in woody shrubs in the small regeneration layer and increases in red pine regeneration in the large regeneration layer. The sapling layer had increased importance of several native tree species, especially in the aggregated treatment.Both of our silvicultural approaches moved structurally simple, species poor red pine plantations toward a condition reflecting native red pine woodlands, which had mixed-species composition and variably open canopy cover. The treatment using variable density thinning and an irregular shelterwood resulted in greater spatial variability of structure and greater importance of shade-intolerant tree species, both characteristics of native woodlands. While focused on red pine, this approach should have application to conifer restoration more generally.

Optimal Regional Allocation of Future Population and Employment under Urban Boundary and Density Constraints: A Spatial Interaction Modeling Approach

This paper develops an optimization modeling framework to select strategies of land development and population and employment densities for a growing metropolitan area. The modeling core involves a non-linear commuting model, which accounts for spatial structure variables and is empirically estimated by Tobit regression. This commuting model is then embedded into a non-linear optimization model that allocates increments in the population and employment (activities) to available land, while minimizing the total future commuting costs under various combinations of land expansion boundaries and population and employment densities. The resulting minimum cost surface is approximated via polynomial regression and combined with land development and congestion cost functions to derive the overall optimal strategy. These models are estimated and calibrated with data from the Census Transportation Planning Package (CTPP) and Auditor’s property database, and are applied to the Fredericksburg metropolitan area, Virginia. The results demonstrate that the optimal development densities are very sensitive to the congestion cost function. A land development strategy that allows for limited sprawl might be a smart policy to reduce both regional vehicle mile travel (VMT) and related congestion and pollution.

A simplified geostatistical approach for characterizing key aspects of short-range roughness

Surface roughness, i.e. local variations in elevation which indicate less and less local smoothness, is an important concept for the analysis of solid earth/planetary surfaces and of the related surface processes. Surface roughness is a general concept covering multiple aspects of the surface spatial variability structure, that can be characterized at different scales and with different metrics. Accordingly, it is not surprising the wide range of approaches and algorithms adopted for the analysis of surface roughness and the lack of an accepted standard for its evaluation. In this context, geostatistical-based roughness indexes are a valid solution, providing a good balance between flexibility of algorithms and interpretability of the results. However, despite the long record of applications and the well-known and robust theoretical framework, geostatistical-based surface roughness tools have still not gained momentum in the context of geomorphometric analysis. Many geomorphometric studies concerning with the evaluation of roughness indexes are based on popular approaches, such as the ones based on measures of dispersion of normal vectors to surface. Unfortunately, these indexes present some drawbacks in the capability to represent specific aspects of surface roughness and from the perspective of their interpretability. Accordingly, in this paper some of the key aspects and advantages of geostatistical-based approaches for roughness analysis are highlighted, including the relevance of roughness anisotropy. Then, a new approach tailored to the analysis of short-range roughness, requiring a minimum intervention by the user, is introduced discussing the advantages compared to the vector dispersion-based roughness. The new approach is implemented as open-source code both in R language using the functions of the “Terra” package as well as in Python for Esri ArcMap GIS. The methodological considerations and algorithms presented can be applied also in the broader context of image analysis.

A new method for modelling precipitation variability in relation to climate change

Abstract Climate change is one of the main consequences of anthropogenic activities. Since the 1950s, gradual changes and an increase in climate warming have been observed. Previous research has been indicating potential associations between climate warming and spatiotemporal changes in precipitation. Moreover, the regional patterns of precipitation have a key role in the continuous monitoring of climate characteristics and natural hazards such as floods and droughts. Therefore, precise and accurate measurements of precipitation concentration and spatiotemporal variability in their patterns are very crucial. In this study, a new method for measuring precipitation concentration is developed and applied to 54 meteorological stations in Pakistan. Furthermore, to assess the precipitation patterns, the proposed method provides solid evidence for considering the effect of temperatures under climate warming. Furthermore, using the spatial correlation between the proposed method and its competitor, a comparative analysis is made to evaluate the performance of the proposed method. Moreover, the spatial variability structure in various precipitation patterns is assessed and compared using spatial predictive maps. Outcomes associated with this research show significant deviations between the proposed method and the existing one. In this paper, regression analysis revealed that the additional input can potentially improve the precipitation estimates under the appropriate sampling estimator. This is the first study that has documented the impact of climate warming on measuring precipitation concentration. These findings can contribute to a better understanding of precipitation concentration in relation to climate warming.

High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems

The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015–2018. SIF spatial variability in each biome presented significant spatial variability structures with high R2 values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m−2 sr−1 μm−1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m−2 sr−1 μm−1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.

Coupled Model Intercomparison Project Phase 6 simulations of the spatial structure of rainfall variability over East Africa: Evaluation and projection

AbstractModifications in rainfall patterns may have significant effects on a variety of natural and human systems. This study evaluates the ability of 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the interannual variability of rainfall over East Africa (EA) using a method based on the empirical orthogonal function (EOF) analysis. The future changes in rainfall variability during the near (2021–2040), middle (2041–2060) and late (2080–2099) future are analysed under two different shared socioeconomic pathways (SSP), SSP2‐4.5 and SSP5‐8.5. Results reveal that most models captured better spatial climatological rainfall pattern than simulated amplitude in the EA region receiving bimodal rainfall pattern (EABM) compared to that with unimodal rainfall regime (EAUM) in the historical period. An ensemble mean of all models (AMME) and a set of 13 models that best simulated the rainfall variability in the base period were selected using a robust method based on the EOF analysis for further analysis. Most of the selected models and their ensemble mean (BMME) displayed good capability in representing the annual standard deviation (SD) in recent decades, whereas BMME corroborates AMME, particularly over the EABM and EAUM regions. Based on these findings, the AMME and BMME were used to evaluate the future changes in rainfall variability. The models project a significant increase in rainfall variability during March by the mid and late 21st century over the EAUM region under SSP5‐8.5, whereas the increase appears much earlier in the near‐future over the EABM region. In all future periods and SSPs, SD demonstrates a considerable increase over most of the EABM region, and the magnitude gradually increases from the AMME to BMME projections. Moreover, a relatively stronger increase is anticipated to actualize by the mid of 21st century.